Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed
This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7305 |
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| author | Krzysztof Przybył Jolanta Wawrzyniak Krzysztof Koszela Franciszek Adamski Marzena Gawrysiak-Witulska |
| author_facet | Krzysztof Przybył Jolanta Wawrzyniak Krzysztof Koszela Franciszek Adamski Marzena Gawrysiak-Witulska |
| author_sort | Krzysztof Przybył |
| collection | DOAJ |
| description | This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN. |
| first_indexed | 2024-03-10T13:56:26Z |
| format | Article |
| id | doaj.art-a4e7d386c69747bca80fec780a3e5e82 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:56:26Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a4e7d386c69747bca80fec780a3e5e822023-11-21T01:37:38ZengMDPI AGSensors1424-82202020-12-012024730510.3390/s20247305Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of RapeseedKrzysztof Przybył0Jolanta Wawrzyniak1Krzysztof Koszela2Franciszek Adamski3Marzena Gawrysiak-Witulska4Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandDepartment of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, PolandFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandThis paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.https://www.mdpi.com/1424-8220/20/24/7305rapeseed storagemouldimage analysisconvolutional neural networksmachine learning |
| spellingShingle | Krzysztof Przybył Jolanta Wawrzyniak Krzysztof Koszela Franciszek Adamski Marzena Gawrysiak-Witulska Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed Sensors rapeseed storage mould image analysis convolutional neural networks machine learning |
| title | Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed |
| title_full | Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed |
| title_fullStr | Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed |
| title_full_unstemmed | Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed |
| title_short | Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed |
| title_sort | application of deep and machine learning using image analysis to detect fungal contamination of rapeseed |
| topic | rapeseed storage mould image analysis convolutional neural networks machine learning |
| url | https://www.mdpi.com/1424-8220/20/24/7305 |
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